eChapters

At a compound annual growth rate (CAGR) of 7.6%, the global poultry industry expanded from $352.02 billion in 2022 to $378.84 billion in 2023. Global production of chicken meat was projected to reach 103.4 million metric tons in 2023, primarily as a result of rising output in China, India, Brazil, and the US. India is currently the world’s second-largest egg producer (138.3 billion eggs in 2022–2023), trailing only China. It is also the world’s fifth-largest broiler producer (producing 4.99 metric tons (MT) in 2022–2023), behind China, the United States, Brazil, and the Russian Federation.

Introduction

The term "artificial intelligence" was originally coined in 1956 at a conference hosted by Dartmouth College. While the aspirations and dreams of the attendees were sky-high, the reality was sobering. The thought of citizens in space or self-driving cars within a decade was ancient science fiction. The building blocks of the required computing infrastructure, as well as AI, notably deep learning and statistical machine learning methods, were not even in the distant future. The aim was to create an artificial brain for a machine, where the "brain" is the so-called "expert system", a software program that can integrate a large knowledge base and use it to solve problems in the same manner that a human expert could. The intellectual merits and achievements of a few AI systems designed solely to narrowly excel in a specific problem became the actual problem of the nascent field.

This chapter integrates case studies, scientific examples, and policy perspectives to illustrate how AI and ML can transform agricultural weather intelligence. The discussion spans from advanced predictive models in developed nations to emerging applications in technology-limited regions, while also addressing the urgent need for resilient crisis management systems in the face of natural and human-caused conditions. Advanced scientific techniques can not only optimize food production and resource management but also play a critical role in recovery efforts during times of crisis. Agriculture is intrinsically linked to the natural world—a domain defined by fluctuating weather patterns, seasonal shifts, and unpredictable environmental factors. Today, Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing how we analyze and predict weather patterns, and their applications in agriculture are far-reaching (Fig. 5.1). From forecasting crop yields and fishery conditions to managing plantations and monitoring livestock health, these technologies are creating a paradigm shift in agricultural weather intelligence. Scientific organizations worldwide are leveraging AI and ML to refine predictive models, enable multi-variate analysis, and provide advanced warning systems for extreme events such as droughts, tsunamis, earthquakes, and even crises caused by human-made conflicts. Emerging nations with rapidly growing technological capabilities stand to benefit immensely from these innovations, tailoring solutions to local challenges while contributing to global food security and crisis resilience.

Introduction
Antimicrobial resistance (AMR) has emerged as a critical global health threat, diminishing the effectiveness of antibiotics against bacterial infections. Conventional strategies to counter AMR—including antibiotic stewardship programs and the development of new drugs—are often slow, expensive, and labor intensive. However, artificial intelligence (AI) and machine learning (ML) present ground breaking alternatives to address this challenge. By leveraging advanced computational techniques, AI can significantly speed up the discovery of novel antibiotics, enabling researchers to identify promising drug candidates more efficiently than traditional methods. Additionally, ML algorithms enhance treatment optimization by analyzing patient data to recommend personalized antibiotic regimens, reducing misuse and resistance risks. AI also strengthens surveillance systems by processing vast datasets— such as genomic sequences and electronic health records—to detect emerging resistance patterns in real time. These capabilities allow for proactive interventions, helping healthcare providers and policymakers stay ahead of AMR outbreaks. Together, AI and ML offer a transformative approach to mitigating AMR, combining speed, precision, and scalability to combat one of the most pressing medical challenges of our time.
AI in Antibiotic Discovery
One of the most promising applications of AI in AMR mitigation is in antibiotic discovery. Traditional drug development is costly and time-consuming, often taking over a decade [1]. Machine learning models can analyse vast datasets of chemical compounds to predict their antibacterial properties. For example, researchers at MIT used deep learning to identify halicin, a novel antibiotic effective against drug-resistant bacteria. AI-driven platforms can also repurpose existing drugs, reducing the time needed for clinical trials [2].
Fig. 1: AI cyclic interface in antibiotic discovery
Predictive Analytics for Resistance Patterns AI-powered predictive analytics enables early detection of antimicrobial resistance (AMR) patterns by processing clinical, genomic, and epidemiological data. Machine learning models analyse electronic health records (EHRs) [3] and bacterial genome sequences to identify emerging resistance threats in real time. These predictive insights allow hospitals to take proactive measures— such as optimizing antibiotic prescriptions—before resistant strains spread. Beyond clinical settings [4], AI models assist public health agencies in forecasting outbreaks and strategically deploying resources. By integrating large-scale datasets, these systems enhance surveillance, enabling faster, datadriven decisions to curb AMR progression. This approach not only improves patient outcomes but also supports global efforts in combating antibiotic resistance through smarter, pre-emptive interventions [5].

1. Introduction
Studies on soil health and agricultural growth are increasingly using artificial intelligence (AI) and machine learning (ML) techniques. With the help of these technologies, it is possible to analyse vast amounts of data and gain insightful knowledge that will help to boost crop yields and optimise agricultural practises (Neethirajan, 2020). Applications of AI and ML to research crop development and soil health have enormous potential to revolutionise agriculture. In order to increase soil health and crop yield, farmers and researchers can use these technologies to analyse massive datasets, generate precise forecasts, and optimise farming practises. Overall, artificial intelligence (AI) and machine learning (ML) provide useful tools that can support efficient and sustainable agriculture, solving global concerns in food availability and the sustainability of the environment. Unquestionably, technical developments have significantly increased the productivity and efficiency of many industries, including agriculture. The advent of words like “big data,” “data analytics,” “artificial intelligence,” “Internet of Things,” “erosion modelling,” “smart farming,” and “machine learning” are just a few examples of this technological revolution (Almoussawi et al., 2022). Digital soil mapping (DSM) uses computational models to infer regional and temporal shifts of soil types and properties based on soil observations, prior knowledge, and relevant environmental variables in order to create and populate spatial soil information systems. The supply chain for agricultural production is extremely intricate (Lamichhane et al., 2019). The way our food is produced, distributed, and consumed is changing as a result of AI. When planning crop rotations, planting times, water and nutrient management, pest and disease control, optimal harvesting, food marketing, product distribution, food safety, and other agriculture-related tasks throughout every step of the food supply chain, researchers use powered by AI tools to supply advice and expertise. In “Harnessing AI to transform agriculture and inform agricultural research,” Peters et al. (2020) presented a summary of the most recent developments, difficulties, and prospects for AI technology in agriculture. They use the four main elements of the food system—production, distribution, consumption, and uncertainty—to show the possibilities of AI. They come to the conclusion that agricultural businesses are excellent candidates for using AI and other technologies. In “AI down on the farm,” Sudduth et al. (2020), examined a number of case studies in which machine learning (ML) has been used to model various aspects of agricultural production systems and give data that can be utilised to make management decisions at the farm level. These research efforts involve providing data, essential for creating precise and effective irrigation systems and improving tools for suggesting the best nitrogen fertilisation rates for maize. Traditional crop health monitoring techniques need a lot of work and time. Using AI to monitor and detect potential crop problems or nutrient deficits in the soil is an effective method. Applications to analyse plant health trends in agriculture are being created with the aid of deep learning. These AI-powered tools are essential for improving our understanding of soil quality, crop pests, and disease in plants (Virnodkar et al., 2020).

Millets have potential as both food and fodders crops. Foods made from millet are abundant in micronutrients and necessary amino acids for regulatory functions. Since, millets were traditionally grown under adverse conditions, a great amount of polymorphism was preserved, making millet crops naturally resistant to abiotic and biotic stresses. Using data-driven technology to increase genetic gain in breeding operations can improve traditional breeding. Artificial intelligence can be used to create varieties that are adapted to certain soil and climatic conditions. In order to achieve breeding goals, machine learning (ML) is crucial in data mining and analysis, providing information for decision-making. We have been able to generate information on interesting genes using data mining and allele identification methods. For the exploitation of the natural variation and the identification of target genes, advances in high-throughput sequencing for the detection of genetic variations as well as the role of phenomics in genomics-assisted breeding can be employed. QTLs and marker-trait correlations can be identified via genome-wide association analyses. Multiple loci and genes that control agronomic traits have can be studied by comparative and functional genomics. Model selection is crucial for ML algorithms in smart breeding, which depends on knowledge in genotype, phenotype, and G×E interaction to be handled through computer science.

Climate change affects livestock through heat stress, water scarcity, unstable feed supplies, altered disease patterns and extreme weather. This chapter examines how artificial intelligence supports climate-smart and sustainable animal husbandry. Machine learning, predictive analytics and IoT-enabled monitoring can optimize feeding, detect disease, assess welfare and improve the use of water, energy and other resources. Intelligent technologies can also assist in reducing greenhouse-gas emissions and developing adaptive production strategies. The chapter evaluates the contribution of AI to productivity, climate resilience and environmental responsibility while addressing problems such as insufficient data, limited infrastructure, implementation costs and ethical concerns. Future integration with sustainable agricultural frameworks is also discussed.

9.1 Introduction

The increase in world’s population and respective food demand has brought the global concern. The climate change alone has put a tremendous burden on the farming communities, and has become the major challenge to feed the burgeoning population. It has been estimated that by the end of the year 2050, the global population will be around 10 billion, meanwhile, the food demand will also be increased by 70-100 %. The traditional method of agricultural practices are not sufficient enough to meet the rising food demand. Therefore, to meet the increasing food demand, agriculture is intensified by the use of chemical fertilizer, pesticides, herbicides, which deteriorat the soil health. Thus, new automated/ mechanised systems have been introduced into the agriculture sector to ensure precision farming. These systems are quick in action, and satisfing the requirements along with generation of billions of employment worldwide. In a developing country like India, 65-70 per cent of the total population resides in rural areas, of which, most of the households are dependent on agriculture. The agriculture sector in India accounts for 18 per cent of the country’s GDP, and provide employment to 50 per cent of the total work force. The agriculture sector is one of the most sensitive sectors of Indian economy which supports all other sectors, and spreading its benefit to the far-reaching areas. Therefore, it becomes important for the developing country like India to develop agriculture sector to boost-up the rural economy.

With the advent of technology, a rapid transformation has been occurred in various industries, while agriculture remains the least digitized. Though, the application of aircraft/ drones in agriculture for fertilizer/ pesticides applications, image capturing, access to real-time quality data has adopted in some developed countries like Australia, USA, Japan, and some of the European countries. Moreover, these unmanned aerial vehicles could provide data of soil health, monitor crop health, assist in irrigation scheduling, yield estimation, and weather data analysis too. However, such development in agriculture is still lacking in developing countries. The agriculture development in the modern era could be done by taking the biological information and agricultural knowledge through integration of modern agriculture information technology with traditional agricultural practices. These knowledge integrations with mobile internet, cloud computing, and machine learning could enable to control the agricultural production system over stressed climate condition easily. Thus, Artificial Intelligence (AI), which is a sort of machine intelligence, an inbuilt set of programme and algorithms similar to the human brain in the current world is gaining more attention in agricultural application for precision farming.

Artificial intelligence (AI) uses multiple technologies that equip machines to sense, comprehend, plan, act, and learn with human-like levels of intelligence. Fundamentally, AI systems perceive environments, recognize objects, contribute to decision making, solve complex problems, learn from past experiences, and imitate patterns. AI technologies such as deep learning allow computer systems to understand a problem, learn from examples, and make predictions.

The AI has a tremendous potential to solve the diverse application problems in the field of domestic animal breeding and management. Till date, most of the studies have been focused on animal behaviour (Valletta, et al., 2017), cognition (Lake et al., 2017), animal welfare (Neethirajan, 2021), individual identification (Norouzzadeh et al., 2018), growth or body condition scoring (Zin et al., 2020), disease detection and its monitoring (Kumar et al., 2021), precision farm management (Radun et al., 2021) etc. particularly in the pigs, poultry and cattle species. These AI based applications primarily involves data generation, data processing, use of gadgets / IoT devices, development of smart machine learning algorithms and their integration to attain precision to solve various issues in animal husbandry. Having said that, very few works have been carried out with respect to applications of AI in domestic animal breeding, and major problems like breed identification, genomics- based animal selection criteria and breeding decision, are yet to be automated. Moreover, there are some bottlenecks in application of AI in animal breeding, like the lower accuracy of a decision/outcome and large cost involved in creating AI based infrastructure.

Introduction
Loss of arable land due to population expansion, urbanization and climate change have substantially impacted agricultural production, driving the need to exploit advanced techniques for enhancing sustainable crop production (Bren d’Amour et al. 2017). The yield and productivity of plants are influenced by both their genotype and the growing environment, which govern the performance indices or phenotype (Reynolds et al. 2020). Conventional plant breeding plays a crucial role in ensuring food and nutritional security for the ever-growing global population by harnessing genetic diversity and selecting plants with superior agronomic traits (Singh et al. 2019). However, identifying plants with superior agronomic traits and novel genes (quantitative trait loci or QTLs) through conventional breeding is akin to finding a needle in a haystack (Bohra et al. 2022). Furthermore, the conventional breeding approach is time-consuming and often associated with linkage drag due to extensive inter-crossing/hybridization among elite, wild, and common landraces (Beaver and Osorno 2009). Despite these challenges, classical breeding does develop new plant varieties with improved traits that can withstand climate extremes. Nevertheless, the duration of breeding cycles required to successfully complete the breeding program remains a major bottleneck, taking around 7-9 years to develop an improved variety (Pratap et al 2021).
Efforts have been made to revamp conventional breeding techniques by exploiting molecular markers (Ribaut et al. 2010). These markers effectively capture genetic variations across large populations at an early plant developmental stage. Additionally, the use of molecular markers in plant breeding significantly reduces the time required to screen plant genotypes with desired traits. Otherwise, achieving this task would take 2-3 years (Amiteye et al. 2021). Molecular marker-based techniques, such as marker-assisted selection (MAS), markertrait association (MTA), and genome- wide association studies (GWAS), have become routine tools. They help predict and develop genetic models for plants containing desired traits (Bohra et al. 2020). Furthermore, plant breeders have successfully integrated these molecular markers with genomics, phenomics, transcriptomics, proteomics, metabolomics, and epigenomics approaches (Malik

Agriculture is the support for the sustainable health and economic growth of the country. With the frequent changes in climatic conditions and increase of global population, it is important to come up with new varieties and nutrient rich crops. In this context, underutilized and neglected millets, considered as “nutri-cereals” or “smart foods” due to better adaptation in diverse environmental stresses and tolerance to insect pests and diseases, also a staple crop for hunger-stricken areas. Millet production is limited by the lack of intensive farming and deployment of genetic tools for trait and nutrition improvement. The recent advancement in machine learning (ML) and Artificial Intelligence (AI) technologies are key to depict plant response to environmental perturbation in huge data sets. Therefore, precision agriculture or smart farming is necessary to explore the genetic makeup of the neglected millets. AI has a substantial potential to handle numerous challenges in the formation of knowledge-based farming systems. The next agricultural revolution would emphasize more on farm machinery using AI integrated with the Internet of Things (IoT). In Smart agriculture, deep learning facilitates fast and interesting data analysis and provides support to researchers and farmers by the outcomes generated from the analysis. Here, we emphasize on the advancement of AI and its application to monitor the underutilized and neglected millets quality, yield and disease assessment. This chapter summarizes how AI can contribute to the improvement of sustainable productivity and quality of underutilized and neglected millet.

Genetic improvement creates permanent, heritable gains in milk production, growth, fertility, disease resistance and environmental adaptation. This chapter explains how artificial intelligence complements conventional phenotypic and pedigree-based selection by analysing large genomic and performance datasets. Machine-learning models reveal genotype-phenotype relationships, predict breeding values and support earlier, more reliable selection. Applications include genomic selection, precision breeding, reproductive technologies, bioinformatics and digital twins. AI can reduce generation intervals and improve the efficiency of breeding programmes, but its success depends on data quality, representative populations and interpretable models. Ethical concerns, genetic diversity and long-term sustainability must also be considered when designing AI-supported breeding systems.

Genetic improvement creates permanent, heritable gains in milk production, growth, fertility, disease resistance and environmental adaptation. This chapter explains how artificial intelligence complements conventional phenotypic and pedigree-based selection by analysing large genomic and performance datasets. Machine-learning models reveal genotype-phenotype relationships, predict breeding values and support earlier, more reliable selection. Applications include genomic selection, precision breeding, reproductive technologies, bioinformatics and digital twins. AI can reduce generation intervals and improve the efficiency of breeding programmes, but its success depends on data quality, representative populations and interpretable models. Ethical concerns, genetic diversity and long-term sustainability must also be considered when designing AI-supported breeding systems.

Currently, the milk production of country is 221.06 million tonnes (2021 22) and the target is 300 million tonnes by 2023-24. The livestock sector contributed nearly 5.21% of total gross value added (GVA) and 28.36% of the agriculture and allied sectors GVA. Thus, there is a huge gap between demand and supply, therefore with the requirements of better yield from the dairy animals, we need more better and advanced options. Artificial intelligence (AI) is one of such options which can be exploited in dairy industry. Artificial Intelligence can be a game-changer for the dairy farming in India and it can emerge as a tool that empowers farmers in monitoring, forecasting as well as optimizing the farm animal growth. It can predominantly change the scenario of dairy farmers by maintaining the health, physiological and physical conditions of dairy-cows. This knowledge-based technology has a huge potential and could confront the loopholes in dairy farming and thus indirectly can strengthen the dairy industry. In dairy farming AI has multiple applications like monitoring the activities of the dairy-cows, boosting the milk production and farm productivity, detection of mastitis in dairy-cows and developing the smart cow houses powered by image analysis.

AI enables the dairy farmers to determine whether the cow is ill, ready to breed or has become less productive. The AI also sends alerts to the farmer about the change in the cow’s behavior allowing human intervention where needed. Regular 24x7 monitoring of dairy animals is the key for successful dairy farming, however, traditional methods of monitoring relied on visual observation, but it was not very efficient due to the lack of time and human resources. Without AI, it would be almost impossible for the farmer to keep a watchful eye on every cow in the herd. Artificial Intelligence components of the dairy automation system process the collected data to provide insights on the heat stress, change in feeding efficiency and the estrus of the cow. Diseases like sub-clinical mastitis, one of the more common diseases in the dairy industry, cost the Indian dairy industry one billion dollars annually. Eventually, it provides new hope and open prospects for the overall quality and progress in the dairy industry through a profitable business approach in dairy farming

What is Artificial Intelligence

‘Artificial’ is taken to mean a property of a human-made machine. ‘Intelligence’ will be that when something is able to perform a new action or change its state to achieve the optimal response it could perform given external inputs. This term is coined by- John McCarthy (American computer and cognitive scientist) during 1956 So, AI is basically a machine able to mimic human intelligence by having the ability to predict, classify, learn, plan, reason and perceive.

AI is an emerging technology in the field of agriculture. AI-based equipment and machines, has taken today’s agriculture system to a different level. This technology has enhanced crop production and improved real-time monitoring, harvesting, processing and marketing . The latest technologies of automated systems using agricultural robots and drones have made a tremendous contribution in the agro-based sector. Various hi-tech computer-based systems are designed to determine various important parameters like weed detection, yield detection and crop quality and many other techniques.

Introduction 
In the past, literacy in reading, writing and mathematics was considered the basis of establishment in society. With this knowledge, anyone could run all the businesses of his professional life efficiently because no technical skills other than typewriter and general computer knowledge were required. In present set up, this general knowledge is no longer enough to perform any task smoothly today. In this age of science and technology, the old concept of literacy is no longer applicable. Three other types of literacy or skills have now been added to the past concept of literacy: information literacy, technological literacy and human literacy. In this age of technology, people cannot keep themselves functional in this digitized world by practical knowledge of analog equipment alone. In present days, people live and work on the constant flow of big data, connections, and instant information flowing from every click and touch of a device. Technology is not static. Evernew technological advancement brings tides of data before people, therefore, they need data literacy to read, analyze and use them. Technological literacy gives them grounding in coding and engineering principles, so they know how their machines tick. Ultimately, human literacy teaches them the humanities, communication and design, allowing them to work in human environments. Artificial intelligence (AI) is now a fact of everyday life in our modern hightech societies. There are many definitions of AI and each of those definitions has been revised over time. Presently, most definitions state that AI solves complex cognitive problems associated with human intelligence or that AI helps as many people as possible through smartphones or healthcare, or even that AI recognizes problems and creates solutions for the benefit of technology, people, and society. However, the core concept of AI has continuously been to create machines that were capable of thinking like humans. It took about 200,000 years for human intelligence to evolve from natural to artificial and only 10 years to cut ties with the Earth and move to the clouds. During the creation of AI, mankind learned a lot about what it means to be human, how human intelligence is structured, and how humans learn and acquire expertise. With more predictive power, more efficiency and the ability to deliver better results than humans, AI is rapidly moving aside from humans into the world of expertise. However, it is impossible to imagine that someone else will replace Homo sapiens at this moment, but its probability cannot be ruled out in the days to come. The day, when mankind, in the course of time, will immerse in the world of knowledge, robotics becomes an essential part of the human body and existence, the existence of mankind is sure to be threatened. In October 2017, Saudi Arabia granted citizenship to humanoid Sofia. Perhaps this is a new breed of human being brought about by technology in the 21st century. Just as the message of the first driverless car aroused a lot of curiosity among mankind and now that curiosity has disappeared completely, it is likely that the arrival of many Sophia’s will make the environment very normal in the coming days. So here comes AI – Artificial Intelligence, the driving force behind Sophia or Driverless cars. The very simple definition of artificial intelligence is -the intelligence displayed by machines. Today, in this age of science and technology, we can say that anything imaginable by humans can be done with the help of machines. Sundar Pichai, CEO Google and Alphabet in his article “The promise of AI for India and the World” opined that every technology shift is an opportunity to advance scientific discovery, accelerate human progress and improve lives. AI will do this on an unprecedented scale and India is uniquely positioned to play a leading role.

Artificial intelligence is founded on the idea that human intelligence may be described in such a way that a computer can simply imitate it and carry out tasks ranging from simple to sophisticated. Artificial intelligence is assisting several agricultural sectors in increasing output and efficiency. In every industry, AI technologies are assisting in overcoming traditional hurdles.

AI intervention in agriculture is assisting farmers in increasing their farming efficiency while reducing harmful environmental impacts. The agriculture business has embraced AI with open arms in order to improve overall results. The way food is produced is changing as a result of artificial intelligence. Incorporating AI technology into agriculture aids in the control and management of any unwelcome natural occurrence.

1.1 Introduction

Since the beginning of humankind, we have thrived as a species due to our unique capabilities. These skills have developed and progressed over the centuries to the point that, in recent decades, we have been able to build machines that can imitate our intelligence, as far as we comprehend it. A novel type of world may lie ahead of us.

Artificial intelligence is the future, and the future is here." - Dave Waters

Artificial intelligence (AI) refers to the replication of human intelligence in computers, which are programmed to think and behave in a manner similar to humans. Literacy, logic, problem-solving, perception, and linguistic appreciation are all manifestations of cognitive abilities. Artificial Intelligence refers to the development of computer systems, computer-controlled robots, or software that are designed to exhibit intelligent behavior similar to that of the human mind. The term artificial intelligence is composed of the word "Artificial," which denotes something created by humans rather than existing naturally, and "Intelligence," which refers to the capacity to gain and utilize knowledge and skills.AI is achieved by the analysis of patterns in the human brain and the evaluation of cognitive processes. The culmination of these studies leads to the creation of sophisticated software and systems with advanced intelligence. The term AI was coined by McCarthy et al. 2006 while working on Dartmouth summer research project in which they proposed that machines have the capability to emulate "every facet of learning or any other characteristic of intelligence."